Predictive Oncology News
Predictive Oncology is streamlining the way medicines go through medical testing, which takes years. The Lawrenceville company’s technology allows drugs to be tested on a much larger scale with fewer patients, increasing the likelihood of success and decreasing time to market.
The company combines Helomics’ large biobank of 150,000 tumor samples and pathology slides with artificial intelligence and Quantitative Medicine’s drug discovery platform. The result is the PEDAL platform that predicts drug response in a heterogeneous patient population against early stage drug compounds.
Using Artificial Intelligence to Drive Personalized Cancer Therapies
Artificial intelligence (AI) has revolutionized cancer diagnosis by accelerating the development of new medications. It is able to detect specific genetic mutations that can lead to cancer in patients and can identify potential new therapies faster than traditional methods, including blood profiling, light microscopy, and imaging tests.
AI is a computational intelligence that uses machine learning and natural language processing to emulate human thought processes. It is a key technology to help reduce healthcare costs by improving efficiency and productivity. It is also expected to boost the accuracy of diagnosis and treatment by eliminating errors that can occur with manual processes.
Predictive Oncology has partnered with Cancer Research UK to develop a platform that utilizes AI and machine learning to accelerate drug discovery by pairing its PEDAL platform with Cancer Research Horizon’s large biobank of 150,000+ tumor samples. The combination of this data and the PEDAL platform allows for more informed drug-tumor type combinations to be selected in pre-clinical testing, thereby increasing the probability of clinical success.
Predictive Oncology’s PEDALTM Platform
Predictive Oncology’s PEDAL platform pairs its own artificial intelligence with a proprietary biobank of more than 150,000 tumor samples. In this way, the company can efficiently screen compounds against tumor features to generate drug response predictions and increase the likelihood of successful drug development.
That’s a huge step forward for pharmaceutical companies that spend about $1.1 billion annually on the development of new drugs. Unfortunately, 95% of them fail in clinical testing.
In the case of cancer, a 2% improvement in drug discovery could mean that more patients receive better treatment.
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Predictive Oncology and Cancer Research Horizons
Using artificial intelligence to speed up preclinical and phase-one drug development by up to 3x could generate an additional $50 billion for the industry. But to make that happen, cancer patients, clinicians and pharmaceutical companies need to be able to identify candidates much sooner than they do now.
That’s where predictive oncology comes in. The company’s PEDAL artificial intelligence platform can evaluate well-characterized tumor samples against a range of drugs to determine which are most likely to succeed in clinical trials.
The result is a powerful tool that can help biopharma develop the right therapy for the right patient, leading to better outcomes.
To that end, Predictive Oncology has partnered with Cancer Research Horizons, the innovation engine at the core of the world’s largest private funder of cancer research, to use its PEDAL platform to accelerate their work. The company’s CEO Pamela Bush and business development manager Dom Pollard join Natalie Stoberman in the studios to discuss this new partnership.
Predictive Oncology’s STREAMWAYTM System
Predictive Oncology’s PEDAL platform leverages a biobank of 150,000 tumor samples to predict drug response across a heterogeneous patient population against early stage drug compounds. This allows partners to increase the likelihood of success during clinical development leading to better therapies and improved outcomes.
The company’s STREAMWAY System connects directly to the facility plumbing system and eliminates the use of canisters, carts and evacuated bottles for the collection and disposal of waste fluids. This helps minimize human intervention, improves safety and enables compliance with Occupational Safety and Health Administration and other regulatory agency safety guidelines.
HC Wainwright maintains its “Buy” rating on the stock, and an $8 price target, as it sees potential for “promising predictive capability.” Currently, the company’s Helomics subsidiary is conducting a multi-year study with UPMC Magee-Womens Hospital to analyze the impact of its AI-driven models of ovarian cancer data on patient short- and long-term survival. The model combines machine learning with a multi-omic approach to cancer and will help determine whether an individual patient’s genomic profile can be used as a basis for personalized medicine and treatment decisions.